



import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def  lowerCamelCase_	(      lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	List[str]				)       ->							List[str]:

							'''simple docstring'''






							return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	str="attention"				)       ->							List[Any]:

							'''simple docstring'''






							A      						=    A      						=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]				)
							A      						=    k_tmp.reshape(k_tmp.shape[0]			,	k_tmp.shape[1] * k_tmp.shape[2]				)
							A      						=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]				)
							A      						=    o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]			,	o_tmp.shape[2]				)
							A      						=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]				)
							A      						=    q_tmp.reshape(q_tmp.shape[0]			,	q_tmp.shape[1] * q_tmp.shape[2]				)
							A      						=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]				)
							A      						=    v_tmp.reshape(v_tmp.shape[0]			,	v_tmp.shape[1] * v_tmp.shape[2]				)
							return k, o, q, v






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	Any=False				)       ->							str:

							'''simple docstring'''






							if split_mlp_wi:
														A      						=    params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
														A      						=    params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
														A      						=    (wi_a, wi_a)
							else:
														A      						=    params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

							A      						=    params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
							return wi, wo






def  lowerCamelCase_	(      lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	Dict				)       ->							Optional[int]:

							'''simple docstring'''






							return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]






def  lowerCamelCase_	(      lowerCAmelCase__	:	dict			,	*, lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	bool			,	lowerCAmelCase__	:	bool = False				)       ->							Optional[int]:

							'''simple docstring'''






							A      						=    traverse_util.flatten_dict(variables['target']				)
							A      						=    {'/'.join(lowerCAmelCase__				): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							A      						=    'encoder/encoder/mlp/wi_0/kernel' in old
							print('Split MLP:'			,	lowerCAmelCase__				)

							A      						=    collections.OrderedDict()

							# Shared embeddings.
							A      						=    old['token_embedder/embedding']

							# Encoder.
							for i in range(lowerCAmelCase__				):
														# Block i, layer 0 (Self Attention).
														A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'			,	'pre_attention_layer_norm'				)
														A						,   A						,   A						,   A      						=    tax_attention_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'			,	'attention'				)
														A      						=    layer_norm
														A      						=    k.T
														A      						=    o.T
														A      						=    q.T
														A      						=    v.T

														# Block i, layer 1 (MLP).
														A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'			,	'pre_mlp_layer_norm'				)
														A						,   A      						=    tax_mlp_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'			,	lowerCAmelCase__				)
														A      						=    layer_norm
														if split_mlp_wi:
																					A      						=    wi[0].T
																					A      						=    wi[1].T
														else:
																					A      						=    wi.T
														A      						=    wo.T
														if scalable_attention:
																					# convert the rel_embedding of each layer
																					A      						=    tax_relpos_bias_lookup(
																					    lowerCAmelCase__			,	lowerCAmelCase__			,	'encoder'				).T

							A      						=    old['encoder/encoder_norm/scale']

							if not scalable_attention:
														A      						=    tax_relpos_bias_lookup(
														    lowerCAmelCase__			,	0			,	'encoder'				).T
														A      						=    tax_relpos_bias_lookup(
														    lowerCAmelCase__			,	0			,	'decoder'				).T

							if not is_encoder_only:
														# Decoder.
														for i in range(lowerCAmelCase__				):
																					# Block i, layer 0 (Self Attention).
																					A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'pre_self_attention_layer_norm'				)
																					A						,   A						,   A						,   A      						=    tax_attention_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'self_attention'				)
																					A      						=    layer_norm
																					A      						=    k.T
																					A      						=    o.T
																					A      						=    q.T
																					A      						=    v.T

																					# Block i, layer 1 (Cross Attention).
																					A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'pre_cross_attention_layer_norm'				)
																					A						,   A						,   A						,   A      						=    tax_attention_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'encoder_decoder_attention'				)
																					A      						=    layer_norm
																					A      						=    k.T
																					A      						=    o.T
																					A      						=    q.T
																					A      						=    v.T

																					# Block i, layer 2 (MLP).
																					A      						=    tax_layer_norm_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	'pre_mlp_layer_norm'				)
																					A						,   A      						=    tax_mlp_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'			,	lowerCAmelCase__				)
																					A      						=    layer_norm
																					if split_mlp_wi:
																												A      						=    wi[0].T
																												A      						=    wi[1].T
																					else:
																												A      						=    wi.T
																					A      						=    wo.T

																					if scalable_attention:
																												# convert the rel_embedding of each layer
																												A      						=    tax_relpos_bias_lookup(lowerCAmelCase__			,	lowerCAmelCase__			,	'decoder'				).T

														A      						=    old['decoder/decoder_norm/scale']

														# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
														if "decoder/logits_dense/kernel" in old:
																					A      						=    old['decoder/logits_dense/kernel'].T

							return new






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	bool				)       ->							int:

							'''simple docstring'''






							A      						=    collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

							# Add what is missing.
							if "encoder.embed_tokens.weight" not in state_dict:
														A      						=    state_dict['shared.weight']

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					A      						=    state_dict['shared.weight']

														if "lm_head.weight" not in state_dict:  # For old 1.0 models.
																					print('Using shared word embeddings as lm_head.'				)
																					A      						=    state_dict['shared.weight']

							return state_dict






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	int				)       ->							str:

							'''simple docstring'''






							A      						=    checkpoints.load_tax_checkpoint(lowerCAmelCase__				)
							A      						=    convert_tax_to_pytorch(
							    lowerCAmelCase__			,	num_layers=config.num_layers			,	is_encoder_only=lowerCAmelCase__			,	scalable_attention=lowerCAmelCase__				)
							A      						=    make_state_dict(lowerCAmelCase__			,	lowerCAmelCase__				)
							model.load_state_dict(lowerCAmelCase__			,	strict=lowerCAmelCase__				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	bool = False			,	lowerCAmelCase__	:	bool = False			,	)       ->							Dict:

							'''simple docstring'''






							A      						=    MTaConfig.from_json_file(lowerCAmelCase__				)
							print(F'''Building PyTorch model from configuration: {config}'''				)
							# Non-v1.1 checkpoints could also use T5Model, but this works for all.
							# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
							if is_encoder_only:
														A      						=    UMTaEncoderModel(lowerCAmelCase__				)
							else:
														A      						=    UMTaForConditionalGeneration(lowerCAmelCase__				)

							# Load weights from tf checkpoint
							load_tax_weights_in_ta(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
							model.save_pretrained(lowerCAmelCase__				)

							# Verify that we can load the checkpoint.
							model.from_pretrained(lowerCAmelCase__				)
							print('Done'				)


if __name__ == "__main__":
				__snake_case     :Any			       =argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
				# Required parameters
				parser.add_argument(
				    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
				)
				parser.add_argument(
				    '--config_file',
				    default=None,
				    type=str,
				    required=True,
				    help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				parser.add_argument(
				    '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
				)
				parser.add_argument(
				    '--scalable_attention',
				    action='store_true',
				    help='Whether the model uses scaled attention (umt5 model)',
				    default=False,
				)
				__snake_case     :Dict			       =parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path,
				    args.config_file,
				    args.pytorch_dump_path,
				    args.is_encoder_only,
				    args.scalable_attention,
				)






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  ["RoFormerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RoFormerForCausalLM",
           "RoFormerForMaskedLM",
           "RoFormerForMultipleChoice",
           "RoFormerForQuestionAnswering",
           "RoFormerForSequenceClassification",
           "RoFormerForTokenClassification",
           "RoFormerLayer",
           "RoFormerModel",
           "RoFormerPreTrainedModel",
           "load_tf_weights_in_roformer",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRoFormerForCausalLM",
           "TFRoFormerForMaskedLM",
           "TFRoFormerForMultipleChoice",
           "TFRoFormerForQuestionAnswering",
           "TFRoFormerForSequenceClassification",
           "TFRoFormerForTokenClassification",
           "TFRoFormerLayer",
           "TFRoFormerModel",
           "TFRoFormerPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FlaxRoFormerForMaskedLM",
           "FlaxRoFormerForMultipleChoice",
           "FlaxRoFormerForQuestionAnswering",
           "FlaxRoFormerForSequenceClassification",
           "FlaxRoFormerForTokenClassification",
           "FlaxRoFormerModel",
           "FlaxRoFormerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






